NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces

ArXi:2602.03901v4 Announce Type: replace The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization. We develop NeuroPareto, a cohesive architecture that integrates rank-centric filtering, uncertainty disentanglement, and history-conditioned acquisition strategies to navigate complex objective landscapes.